Xue Deng, Mingming Guo, Yi Zhang, Ye Tian, Jingrun Wu, Heng Wang, Hua Zhang, Jialing Le
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The model uses a residual neural network module as the backbone, uses jump connection to improve model generalization, and uses the U-shaped structure to fuse the receptive field features with different scales to enhance the feature expression ability of the model. To prevent improper assumptions from leading to wrong method constraints, we consider the flow characteristic mechanism of each physical field to constrain the neural network and verify its accuracy through numerical simulation of the unsteady flow field in the scramjet combustor with Mach number (Ma) 2.0. This method can accurately predict the multi-physical field of unsteady turbulent combustion based on the time, space, Ma and turbulent eddy viscosity coefficients of a small number of samples. Specially, the proposed physical driven and data driven fusion proxy model can predict the unsteady combustion flow field in milliseconds. 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引用次数: 0
摘要
为了缓解纯数据驱动神经网络模型中的高保真数据依赖性和不可解释性问题,物理信息神经网络(PINN)提供了一种新的学习范式。本研究基于纳维-斯托克斯(Navier-Stokes,NS)方程约束条件,构建了一个高效、准确、稳健的 PINN 框架,用于预测非稳态燃烧流场。为了实现对scramjet燃烧室中多物理场的快速预测,我们提出了一种基于特征信息融合的U型残差神经网络模型。该模型以残差神经网络模块为骨干,利用跳跃连接提高模型泛化能力,并利用 U 型结构融合不同尺度的感受野特征,增强模型的特征表达能力。为防止不当假设导致方法约束错误,我们考虑了各物理场的流动特征机理来约束神经网络,并通过对马赫数(Ma)为 2.0 的争气式喷气燃烧器内的非稳定流场进行数值模拟来验证其准确性。该方法可以根据少量样本的时间、空间、马赫数和湍流涡粘系数准确预测非稳定湍流燃烧的多物理场。特别是,所提出的物理驱动和数据驱动融合代理模型可在毫秒级时间内预测非稳定燃烧流场。这对解决传统燃烧过程数值模拟方法计算效率低的问题具有重要的参考价值。
Intelligent reconstruction of unsteady combustion flow field of scramjet based on physical information constraints
To alleviate the problem of high-fidelity data dependence and inexplicability in pure data-driven neural network models, physical informed neural networks (PINNs) provide a new learning paradigm. This study constructs an efficient, accurate, and robust PINN framework for predicting unsteady combustion flow fields based on Navier–Stokes (NS) equation constraints. To achieve fast prediction of a multi-physical field in a scramjet combustion chamber, we propose a U-shaped residual neural network model based on feature information fusion. The model uses a residual neural network module as the backbone, uses jump connection to improve model generalization, and uses the U-shaped structure to fuse the receptive field features with different scales to enhance the feature expression ability of the model. To prevent improper assumptions from leading to wrong method constraints, we consider the flow characteristic mechanism of each physical field to constrain the neural network and verify its accuracy through numerical simulation of the unsteady flow field in the scramjet combustor with Mach number (Ma) 2.0. This method can accurately predict the multi-physical field of unsteady turbulent combustion based on the time, space, Ma and turbulent eddy viscosity coefficients of a small number of samples. Specially, the proposed physical driven and data driven fusion proxy model can predict the unsteady combustion flow field in milliseconds. It has important reference value to solve the problem of low calculation efficiency of a traditional numerical simulation method of a combustion process.